[
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        "text": "Bank Good Credit "
    },
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        "text": "Accredited with IABAC\u2122"
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        "text": "( International Association of Business Analytics Certifications)`"
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        "text": "\u00a9 DataMites\u2122. All Rights Reserved | www.datamites.com"
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    {
        "type": "Title",
        "element_id": "2dc308bd8d3a5c745dfacc3bdccd81db",
        "metadata": {
            "filename": "Bank Good Credit Loan.pptx",
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            "page_number": 2
        },
        "text": "Objective & Background"
    },
    {
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        "element_id": "5a0a7e2a14285297ff3752656cb6df44",
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            "category_depth": 1,
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            "page_number": 2
        },
        "text": "Classify credit card customers as good / bad, based on information from internal and external sources. "
    },
    {
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        "element_id": "5eb6ec96e6a3493c1ae56747ae457b7f",
        "metadata": {
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        },
        "text": "Data provided"
    },
    {
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            "filetype": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
            "parent_id": "5eb6ec96e6a3493c1ae56747ae457b7f",
            "category_depth": 2,
            "languages": [
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            "page_number": 2
        },
        "text": "Demographic: Base file of with credit card history details. Only one record for every customer."
    },
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        "element_id": "c26d6dc6982b7f42045f4ffee951f8e0",
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            "category_depth": 2,
            "languages": [
                "eng"
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            "page_number": 2
        },
        "text": "Account: Contians data for various loans availed by the customer. Not related to credit card. Multiple records for every customer."
    },
    {
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        },
        "text": "Enquiries: Enquired made by customers for different loan purposes. Multiple records for every customer.\t"
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        "text": "\u00a9 DataMites\u2122. All Rights Reserved | www.datamites.com"
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        "type": "Title",
        "element_id": "0072e6b934945d5ba08f9729e0084739",
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            "category_depth": 0,
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            "page_number": 3
        },
        "text": "Design"
    },
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        "metadata": {
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            "category_depth": 0,
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        "text": "Data to be downloaded using SQL queries."
    },
    {
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        "element_id": "51d8e67259ab8a11d2fdfc5cb9bcf45e",
        "metadata": {
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            "category_depth": 0,
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            "page_number": 3
        },
        "text": "Required information to be extracted from Account and Enquiry files and converted to one-to-one files."
    },
    {
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        "element_id": "6165e3bc219556f6ec397adc7240386b",
        "metadata": {
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            "category_depth": 0,
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            "page_number": 3
        },
        "text": "The columns from the two files should be merged with Demographic file using Left Join with \u201ccustomer no\u201d as key column, to create a final file. The final file should contain all the records in demographic and additional columns/features from Account and Enquiry files will get added to Demographic file."
    },
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            "page_number": 3
        },
        "text": "There will be many customers in account and enquiry file who will get left out. This is fine as we anyway don\u2019t know their good/bad label for training purpose. "
    },
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        "text": "\u00a9 DataMites\u2122. All Rights Reserved | www.datamites.com"
    },
    {
        "type": "Title",
        "element_id": "ed83647ab77addbea9e4dca5f7d8f216",
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            "page_number": 4
        },
        "text": "Analysis of Data"
    },
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        "text": "Show using Excel File"
    },
    {
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            "page_number": 5
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        "text": "Explain Coding / outcomes "
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            "category_depth": 1,
            "languages": [
                "eng"
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            "page_number": 5
        },
        "text": "Show using Jupyter"
    },
    {
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            "page_number": 6
        },
        "text": "Thank You"
    },
    {
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        "text": "\u00a9 DataMites\u2122. All Rights Reserved | www.datamites.com"
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